In order to track and estimate 3D head-shoulder poses in monocular videos,we propose a new 3D head-shoulder model based particle filter.Edge cue and chamfer distance are used to build the likelihood function of the proposed particle filter.In each time step,the proposed algorithm generates a discretized grid state space and takes a subspace of it as the searching space of particles.Therefore,the number of particles is well limited and the performance of the particle filter is improved.The experimental results prove that our method is effective and robust for the tracking and estimation of 3D head-shoulder poses.The study provides a new basis for the human pose analysis and behavior understanding.